1. Parallelization in biomedical time series analysis web platform: the MULTISAB project experience
Alan Jovic1, Kresimir Jozic2, Davor Kukolja1,
Kresimir Friganovic1, Mario Cifrek 1
to:
1 University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia
2 INA - industrija nafte, d.d., Zagreb, Croatia

2. CONTENT Motivation & goal MULTISAB platform structure
Parallelization candidate locations
Parallelization implementation
Validation od cardiac rhythm records
Conclusion

3. Motivation & goal
Complexity of biomedical time series (BTS) processing
The need for efficient web-based biomedical software is continuosly growing in the healthcare community
Goal: development of a web platform for automatic classification of human body disorders based on the analysis of biomedical signals
Achieving efficient calculations through parallelization is an important aspect of the platform!

4. MULTISAB platform structure
Three (sub)projects:
Frontend
browser based UI
Backend
requests & session handling
database communication
Processing
BTS analysis frameworks
Frameworks
Record input handling
Preprocessing
Signal visualization
General time series features
Specific (domain) time series features
Feature extraction
Expert system recommendations
Data mining
Reporting

5. MULTISAB platform structure
Extensive use of many contemporary technologies

6. MULTISAB platform structure

7. Parallelization candidate locations
The computationally most intensive parts of the process
Preprocessing
Filtering, power density estimates, time-frequency methods
Feature extraction
Focus of this work
Data mining
Classification algorithms (ANN, SVM, random forest)

8. Parallelization candidate locations
Feature extraction
Attempt at parallelization of BTS features calculation directly on GPU using Aparapi API (Java OpenCL library)
FAILED
Main reasons for failure:
the nature of the algorithms (conditional next-step execution, non-trivial mathematical operations)
overhead of run-time generation of OpenCL kernel due to code dissasembly
Conclusion: Java multi-threading should be used instead

9. Parallelization implementation
Assumptions for starting feature extraction:
One or more patient records uploaded and preprocessed earlier
Records may contain heterogeneous signals, e.g. 10 EEG trails, 2 ECG trails
Number of feature extraction iterations is equal to the number of different signal types (i.e. ECG, EEG...) in the records.

10. Parallelization implementation
Assumptions for starting feature extraction:
All records contain the same types and numbers of signal trails.
Each iteration is performed on one signal type in all records
All signals in all records are of equal duration, if not, extraction is performed only until the end of the shortest signal trail
Note: user may select only a portion of uploaded records and signals for analysis

11. Parallelization implementation
Feature extraction parameters need to be set:
The list of features that need to be extracted for each iteration
The list of feature parameters with values for each feature
The starting time in the record from which the feature extraction process starts (the same for all iterations).
The analyzed segment width (the same for all iterations).
The final time in the record until which the analysis is performed (the same for all iterations).

12. Parallelization implementation
Parallelization procedure
if (no. segments in signal > 1), process segments in parallel until all are analyzed
else if (no. signals of the same type > 1), process signals in parallel until all the signals are analyzed*
else if (no. records > 1), process records in parallel until the records are analyzed.
else // only one record with one signal type and one segment
the parallelization is not performed and the record is analyzed in the main thread.
* Note: a special case for the implemented bivariate features is that all signal pairs (e.g. for calculation of the mutual information feature) are analyzed in parallel.

13. Parallelization implementation
Extraction of the list of features in a single segment, signal, or record always proceeds sequentially (for easier synchronization due to varying algorithms’ complexities)
Limit the degree of parallelization: no. logical cores – 1
Synchronization: all threads need to finish with the current feature extraction task before moving on to the next parallelization task (main thread waits).

14. Parallelization validation
Cardiac rhythm records from well-known MIT-BIH Arrhythmia Database available at PhysioNet web portal
A total of 27 time domain, frequency domain and nonlinear heart rate variability features (standardly used as well as experimentally used features)
The experiments were run on Intel Core i GHz with 16 GB RAM and 8 logical cores, of which 7 were used for parallelization.

15. Parallelization validation
No. of analyzed record segments,
with their length
Parallel / sequential execution
Number of included records 1 12 48
85 segments, each 20 s
parallel
11630
35714
113579
sequential
10314
39315
130466
18 segments, each 90 s
14513
4479
150623
1788
96229
337213
7 segments, each 240 s
20017
89043
251912
26616
197320
800242
3 segments, each 560 s
3157
196623
7858204
53116
4268117
17672115
2 segments, each 840 s
4667
355649
14781244
76629
618155
26395223
Experiments run 5 times, mean and standard deviation are reported in milliseconds

16. Conclusions
Maximum effect of multithreading for a general BTS analysis may be achieved for:
long segments
the number of segments given as multiple of the number of used logical cores
Except for the analysis of a few records with very short segments, parallelization is beneficial

17. Conclusions Future work: Increase degree of parallelization
Implement parallelization for specific preprocessing and data mining methods
Explore efficient parallelization of multivariate features

18. Thank you!
Questions?
This work has been fully supported by the Croatian Science Foundation under the project number UIP : A software system for parallel analysis of multiple heterogeneous time series with application in biomedicine (MULTISAB)